Title :
Rule extraction from neural networks via decision tree induction
Author :
Sato, Makoto ; Tsukimoto, Hiroshi
Author_Institution :
Res. & Dev. Center, Toshiba Corp., Kawasaki, Japan
Abstract :
Rule extraction from neural networks is the task for obtaining comprehensible descriptions that approximate the predictive behavior of neural networks. Rule-extraction algorithms are used for both interpreting neural networks and mining the relationship between input and output variables in data. This paper describes a new rule extraction algorithm that extracts rules that contain both continuous (real-valued) and discrete literals. This algorithm decomposes a neural network using decision trees and obtains production rules by merging the rules extracted from each tree. Results tested on the databases in UCI repository are presented
Keywords :
data mining; decision trees; learning by example; neural nets; UCI repository; continuous literals; data mining; databases; decision tree induction; decision trees; discrete literals; neural networks; predictive behavior; production rules; real-valued literals; rule-extraction algorithms; Artificial neural networks; Data mining; Databases; Decision trees; Electronic mail; Merging; Neural networks; Production; Testing; Training data;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938448
